A New Way to Build Oblique Decision Trees Using Linear Programming

Summary

Adding linear combination splits to decision trees allows multivariate relations to be expressed more accurately and succinctly than univariate splits alone. In order to determine an oblique hyperplane which distinguishes two sets, linear programming is proposed to be used. This formulation yields a straightforward way to treat missing values. Computational comparison of that linear programming approach algorithm with classical univariate split algorithms proofs the interest of this method.

Trees using oblique hyperplanes to partition data are called oblique decision trees and noted ODT.